Data Analytics for Open Software Product Innovation

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Innovation is the principle required for survival in competitive markets and software products are no exception. Rather, software is now developed in an open environment, where open access to knowledge, information and resources far beyond the traditional organizational barriers offers new opportunities to create innovative software products. These innovations are especially pronounced in terms of software functionality, quality and timely delivery. This proposal is devoted to new concepts, theories, processes and methods required to facilitate Open Software Product Innovation. By Open I mean openness in access to information, openness in access to resources, and openness in the involvement of users in creating new products. Product Innovation refers to (software and software-based) products with “incrementally new” and “completely new” features. The long term goal of this proposal is to perform interdisciplinary research to discover new ways to foster software product innovation. The main idea of the proposal is that product innovation can be facilitated by (i) the idea to synthesize former experience into pattern-based recommendations for delivering the right product at the right time to the right groups of users, (ii) incremental innovation on features and product quality based on continuously analyzed usage and demand, (iii) the continuous analysis of real-time data in an open environment including users, developers and customers, and (iv) manageable enlargement of crowd wisdom for incremental innovation and continuous evaluation. We are looking at four areas of discovery to facilitate Open Product Innovation: Hierarchical approach for product innovation combining pattern-based recommendations (in the big) and optimization (in the small). Analytics for F and NF-incremental feature design and development, instead of yes or no decisions towards features. Social media and repository analysis for predictive and prescriptive modeling of feature needs, usage and usefulness. Dynamic decision support for crowdsourced open product development. The research is speculative in the sense that it combines and adjusts methods from different areas of research in a unique way to make product innovation more systematic and data-driven. In particular, this includes methods and techniques coming from other areas and disciplines such as economics, product design, optimization and artificial intelligence. Results of the research are seeded into a living lab learning and training environment called living lab LILAPI. Besides attracting students and encouraging them to get involved in research projects, the living lab is also designed to foster interdisciplinary collaboration. As one of the key applications, LILAPI can provide a semi-realistic environment for learning and training of using mobile apps for rapid decision-making in simulated emergency events.